Collaborative Agent Gameplay in the Pandemic Board Game

TL;DR

This paper introduces an AI agent for the collaborative pandemic board game that uses a Rolling Horizon Evolutionary Algorithm to develop effective strategies in a stochastic environment, improving win consistency.

Contribution

It presents a novel AI approach applying a Rolling Horizon Evolutionary Algorithm to control all players in a cooperative board game, balancing risk and reward.

Findings

The algorithm finds winning strategies more consistently across different game difficulties.

State evaluation metrics influence the balance between optimistic and pessimistic strategies.

The approach effectively manages stochasticity and reduces complexity in game-state analysis.

Abstract

While artificial intelligence has been applied to control players' decisions in board games for over half a century, little attention is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to overcome challenges posed by events occurring during the game's progression. This paper proposes an artificial agent which controls all players' actions and balances chances of winning versus risk of losing in this highly stochastic environment. The agent applies a Rolling Horizon Evolutionary Algorithm on an abstraction of the game-state that lowers the branching factor and simulates the game's stochasticity. Results show that the proposed algorithm can find winning strategies more consistently in different games of varying difficulty. The impact of a number of state evaluation metrics is explored, balancing between optimistic strategies that favor winning and pessimistic strategies that guard against losing.